Checking availability of months of data for states in SDC, looking at the curated data S3 bucket instead of Redshift.
Building off of AWS CLI commands such as:
aws s3 ls s3://prod-dot-sdc-curated-911061262852-us-east-1/
# for one state:
aws s3 ls s3://prod-dot-sdc-curated-911061262852-us-east-1/waze/version=20171031/content/state=TX/table=alert/projection=redshift/year=2017/Version as of 2018-07-20. First just tally which months have some content. We expect to see green for each state, up to the month of today’s date (2019-03-04).
# Loop over states to find which ones have complete data in 2018, using new production version
avail.mo = vector()
use.states = sort(c(state.abb, c("DC", "CA1", "CA2", "CA3")))
# use.states %>% filter(-"CA")
find.months = formatC(1:12, width = 2, flag = "0")
for(i in use.states){
cmd = paste0('aws s3 ls s3://prod-dot-sdc-curated-911061262852-us-east-1/waze/version=20180720/content/state=', i ,'/table=alert/projection=redshift/year=2018/')
mo_i <- system(cmd, intern = T)
avail.mo_i <- substr(mo_i, start =nchar(mo_i[1])-2, stop = nchar(mo_i[1])-1)
avail.mo = rbind(avail.mo, c(i, find.months %in% avail.mo_i))
}
avail.mo <- as.data.frame(avail.mo)
colnames(avail.mo) = c("State", find.months)
DT::datatable(avail.mo) %>% formatStyle(2:13, background = styleEqual('TRUE', 'lightgreen'))Now looking in the contents of each month/state, counting objects and total size of contents.
# Loop over states and months, to find which ones have complete days of data in 2018, using new production version.
# Also track object number and size of month folder.
# Load previously run data if done for the recent few days
today = Sys.Date()
lastweek = as.Date(Sys.Date()-7)
indir = dir()[grep("Curated_Data_2018_", dir())]; indir = indir[grep(".csv$", indir)]
date.indir = as.Date(substr(indir, start = 19, stop = 28), format = "%Y-%m-%d")
date.indir = date.indir[between(date.indir, lastweek, today)]
if(length(date.indir) > 0 ){
df <- readr::read_csv(paste0("Curated_Data_2018_", date.indir[1], ".csv"))
} else {
avail.mo = size.mo = obj.mo = vector()
find.months = formatC(1:12, width = 2, flag = "0")
use.states = sort(c(state.abb, c("DC", "CA1", "CA2", "CA3")))
# use.states = c("AK", "SD")
statmo <- expand.grid(state = use.states, mo = find.months, stringsAsFactors = F)
statmo <- statmo[order(statmo$state),]
for(i in 1:nrow(statmo)){
# cat(state, "\n") i = 1;
mo = statmo[i, "mo"]; state = statmo[i, "state"]
cmd = paste0('aws s3 ls s3://prod-dot-sdc-curated-911061262852-us-east-1/waze/version=20180720/content/state=', state ,'/table=alert/projection=redshift/year=2018/month=', mo, "/")
mo_i <- system(cmd, intern = T)
avail.day_i <- substr(mo_i, start = nchar(mo_i[1])-2, stop = nchar(mo_i[1])-1)
size_cmd = paste0('aws s3 ls s3://prod-dot-sdc-curated-911061262852-us-east-1/waze/version=20180720/content/state=', state ,'/table=alert/projection=redshift/year=2018/month=', mo, "/ --recursive --summarize")
size_i <- system(size_cmd, intern = T)
avail.mo = c(avail.mo, max(as.numeric(avail.day_i)))
size.mo = c(size.mo, as.numeric(gsub("^(\\s*)+Total Size: ", "", size_i[length(size_i)])))
obj.mo = c(obj.mo, gsub("^(\\s*)+Total Objects: ", "", size_i[length(size_i)-1]))
}
avail.mo[avail.mo==-Inf] = NA
df <- data.frame(State = rep(use.states, each = length(find.months)),
Month = rep(find.months, times = length(use.states)),
Days = as.numeric(avail.mo),
Size = size.mo,
Objects = as.numeric(obj.mo))
size.h = vector()
for(i in 1:length(size.mo)){
smi = size.mo[i]
class(smi) = 'object_size'
size.h = c(size.h, format(smi, units = "auto"))
}
df$Size.h = size.h
df$Value = paste(df$Days, df$Size.h, format(df$Objects, big.mark = ","), sep = " ")
df$Value[is.na(df$Days)] = NA
write.csv(df, file = paste0('Curated_Data_2018_', Sys.Date(), '.csv'), row.names = F)
}
df2 <- df %>%
select(State, Month, Value) %>%
spread(key = Month, value = Value)
DT::datatable(df2)# %>% formatStyle(2:13, color = styleEqual('<NA>', 'grey20'))library(plotly)
#ggplot(df, aes(x = Month, y = Objects, fill = State)) +geom_dotplot(binaxis = "y", stackdir='center', position = 'dodge')
df$usetext <- with(df, paste(State, "\n", Month, "\n", format(Objects, big.mark = ",", digits = 0, scientific = F), "Objects", "\n", Size.h))
focal_states = c("CT", "MA", "MD", "NY", "PA", "TN", "TX", "UT", "WA")
gp.focal <- ggplot(df %>% filter(State %in% focal_states),
aes(x = Month,
y = Objects,
group = State,
text = usetext)) + theme_bw()
gp.focal2 <- gp.focal + geom_line(aes(color = State),
alpha = 0.8,
size = 2) +
theme(axis.text.x = element_text(size=7, angle=45),
axis.text.y = element_text(size=7, angle=45)) +
ylab("Count of Objects in S3") +
xlab(" ") +
guides(color=guide_legend(title="State")) +
ggtitle("Count of objects in curated bucket for selected states \n 2018")
#gp.focal2
ggplotly(gp.focal2, tooltip = "usetext", hovermode="compare")library(plotly)
#ggplot(df, aes(x = Month, y = Objects, fill = State)) +geom_dotplot(binaxis = "y", stackdir='center', position = 'dodge')
df$usetext <- with(df, paste(State, "\n", Month, "\n", format(Objects, big.mark = ",", digits = 0, scientific = F), "Objects", "\n", Size.h))
gp.focal <- ggplot(df %>% filter(State %in% focal_states),
aes(x = Month,
y = Size/1000000,
group = State,
text = usetext)) + theme_bw()
gp.focal2 <- gp.focal + geom_line(aes(color = State),
alpha = 0.8,
size = 2) +
theme(axis.text.x = element_text(size=7, angle=45),
axis.text.y = element_text(size=7, angle=45)) +
ylab("Size (Mb) of monthly directory in S3") +
xlab(" ") +
guides(color=guide_legend(title="State")) +
ggtitle("Size of monthly folders in curated bucket for selected states \n 2018")
#gp.focal2
ggplotly(gp.focal2, tooltip = "usetext")